Model-free reinforcement learning (RL) has achieved superhuman performance on many decision-making problems [1]}, [2]}. Typically, the agent learns from trial and error and requires minimal prior knowledge of the environment. Such a paradigm features significant advantages in mastering essential skills for complex systems, but concerns about the systematic safety limit the extent of adoption of model-free RL in real-world applications, such as human-robot collaboration [3]} and autonomous driving [4]}.
